{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Network (Multilayer Perceptron) Demo\n",
    "\n",
    "_Source: 🤖[Homemade Machine Learning](https://github.com/trekhleb/homemade-machine-learning) repository_\n",
    "\n",
    "> ☝Before moving on with this demo you might want to take a look at:\n",
    "> - 📗[Math behind the Neural Networks](https://github.com/trekhleb/homemade-machine-learning/tree/master/homemade/neural_network)\n",
    "> - ⚙️[Neural Network Source Code](https://github.com/trekhleb/homemade-machine-learning/blob/master/homemade/neural_network/multilayer_perceptron.py)\n",
    "\n",
    "**Artificial neural networks (ANN)** or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. The neural network itself isn't an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs. Such systems \"learn\" to perform tasks by considering examples, generally without being programmed with any task-specific rules.\n",
    "\n",
    "A **multilayer perceptron (MLP)** is a class of feedforward artificial neural network. An MLP consists of, at least, three layers of nodes: an input layer, a hidden layer and an output layer. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. MLP utilizes a supervised learning technique called backpropagation for training. Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable.\n",
    "\n",
    "> **Demo Project:** In this example we will train handwritten digits (0-9) classifier using simple multilayer perceptron."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To make debugging of multilayer_perceptron module easier we enable imported modules autoreloading feature.\n",
    "# By doing this you may change the code of multilayer_perceptron library and all these changes will be available here.\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# Add project root folder to module loading paths.\n",
    "import sys\n",
    "sys.path.append('../..')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Dependencies\n",
    "\n",
    "- [pandas](https://pandas.pydata.org/) - library that we will use for loading and displaying the data in a table\n",
    "- [numpy](http://www.numpy.org/) - library that we will use for linear algebra operations\n",
    "- [matplotlib](https://matplotlib.org/) - library that we will use for plotting the data\n",
    "- [math](https://docs.python.org/3/library/math.html) - math library that we will use to calculate sqaure roots etc.\n",
    "- [neural_network](https://github.com/trekhleb/homemade-machine-learning/blob/master/homemade/neural_network/multilayer_perceptron.py) - custom implementation of multilayer perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import 3rd party dependencies.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import math\n",
    "\n",
    "# Import custom multilayer perceptron implementation.\n",
    "from homemade.neural_network import MultilayerPerceptron"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Data\n",
    "\n",
    "In this demo we will be using a sample of [MNIST dataset in a CSV format](https://www.kaggle.com/oddrationale/mnist-in-csv/home). Instead of using full dataset with 60000 training examples we will use cut dataset of just 10000 examples that we will also split into training and testing sets.\n",
    "\n",
    "Each row in the dataset consists of 785 values: the first value is the label (a number from 0 to 9) and the remaining 784 values (28x28 pixels image) are the pixel values (a number from 0 to 255)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "   label  1x1  1x2  1x3  1x4  1x5  1x6  1x7  1x8  1x9  ...    28x19  28x20  \\\n",
       "0      5    0    0    0    0    0    0    0    0    0  ...        0      0   \n",
       "1      0    0    0    0    0    0    0    0    0    0  ...        0      0   \n",
       "2      4    0    0    0    0    0    0    0    0    0  ...        0      0   \n",
       "3      1    0    0    0    0    0    0    0    0    0  ...        0      0   \n",
       "4      9    0    0    0    0    0    0    0    0    0  ...        0      0   \n",
       "5      2    0    0    0    0    0    0    0    0    0  ...        0      0   \n",
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       "7      3    0    0    0    0    0    0    0    0    0  ...        0      0   \n",
       "8      1    0    0    0    0    0    0    0    0    0  ...        0      0   \n",
       "9      4    0    0    0    0    0    0    0    0    0  ...        0      0   \n",
       "\n",
       "   28x21  28x22  28x23  28x24  28x25  28x26  28x27  28x28  \n",
       "0      0      0      0      0      0      0      0      0  \n",
       "1      0      0      0      0      0      0      0      0  \n",
       "2      0      0      0      0      0      0      0      0  \n",
       "3      0      0      0      0      0      0      0      0  \n",
       "4      0      0      0      0      0      0      0      0  \n",
       "5      0      0      0      0      0      0      0      0  \n",
       "6      0      0      0      0      0      0      0      0  \n",
       "7      0      0      0      0      0      0      0      0  \n",
       "8      0      0      0      0      0      0      0      0  \n",
       "9      0      0      0      0      0      0      0      0  \n",
       "\n",
       "[10 rows x 785 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data.\n",
    "data = pd.read_csv('../../data/mnist-demo.csv')\n",
    "\n",
    "# Print the data table.\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Columns: 785 entries, label to 28x28\n",
      "dtypes: int64(785)\n",
      "memory usage: 59.9 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the Data\n",
    "\n",
    "Let's peek first 25 rows of the dataset and display them as an images to have an example of digits we will be working with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How many numbers to display.\n",
    "numbers_to_display = 25\n",
    "\n",
    "# Calculate the number of cells that will hold all the numbers.\n",
    "num_cells = math.ceil(math.sqrt(numbers_to_display))\n",
    "\n",
    "# Make the plot a little bit bigger than default one.\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "# Go through the first numbers in a training set and plot them.\n",
    "for plot_index in range(numbers_to_display):\n",
    "    # Extract digit data.\n",
    "    digit = data[plot_index:plot_index + 1].values\n",
    "    digit_label = digit[0][0]\n",
    "    digit_pixels = digit[0][1:]\n",
    "\n",
    "    # Calculate image size (remember that each picture has square proportions).\n",
    "    image_size = int(math.sqrt(digit_pixels.shape[0]))\n",
    "    \n",
    "    # Convert image vector into the matrix of pixels.\n",
    "    frame = digit_pixels.reshape((image_size, image_size))\n",
    "    \n",
    "    # Plot the number matrix.\n",
    "    plt.subplot(num_cells, num_cells, plot_index + 1)\n",
    "    plt.imshow(frame, cmap='Greys')\n",
    "    plt.title(digit_label)\n",
    "    plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
    "\n",
    "# Plot all subplots.\n",
    "plt.subplots_adjust(hspace=0.5, wspace=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the Data Into Training and Test Sets\n",
    "\n",
    "In this step we will split our dataset into _training_ and _testing_ subsets (in proportion 80/20%).\n",
    "\n",
    "Training data set will be used for training of our model. Testing dataset will be used for validating of the model. All data from testing dataset will be new to model and we may check how accurate are model predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data set on training and test sets with proportions 80/20.\n",
    "# Function sample() returns a random sample of items.\n",
    "pd_train_data = data.sample(frac=0.8)\n",
    "pd_test_data = data.drop(pd_train_data.index)\n",
    "\n",
    "# Convert training and testing data from Pandas to NumPy format.\n",
    "train_data = pd_train_data.values\n",
    "test_data = pd_test_data.values\n",
    "\n",
    "# Extract training/test labels and features.\n",
    "num_training_examples = 1700\n",
    "\n",
    "x_train = train_data[:num_training_examples, 1:]\n",
    "y_train = train_data[:num_training_examples, [0]]\n",
    "\n",
    "x_test = test_data[:, 1:]\n",
    "y_test = test_data[:, [0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Init and Train MLP Model\n",
    "\n",
    "> ☝🏻 This is the place where you might want to play with model configuration.\n",
    "\n",
    "> ⚠️ Be aware though that the training of the neural network with current parameters may take up to 15 minutes depending on the hardware. \n",
    "\n",
    "- `layers` - configuration of the multilayer perceptron layers (array of numbers where every number represents the number of nayron in specific layer).\n",
    "- `max_iterations` - this is the maximum number of iterations that gradient descent algorithm will use to find the minimum of a cost function. Low numbers may prevent gradient descent from reaching the minimum. High numbers will make the algorithm work longer without improving its accuracy.\n",
    "- `regularization_param` - parameter that will fight overfitting. The higher the parameter, the simplier is the model will be.\n",
    "- `normalize_data` - boolean flag that indicates whether data normalization is needed or not.\n",
    "- `alpha` - the size of gradient descent steps. You may need to reduce the step size if gradient descent can't find the cost function minimum. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Configure neural network.\n",
    "layers = [\n",
    "    784, # Input layer - 28x28 input pixels.\n",
    "    25,  # First hidden layer - 25 hidden units.\n",
    "    10,  # Output layer - 10 labels, from 0 to 9.\n",
    "];\n",
    "normalize_data = True  # Flag that detects whether we want to do features normalization or not.\n",
    "epsilon = 0.12  # Defines the range for initial theta values.\n",
    "max_iterations = 300  # Max number of gradient descent iterations.\n",
    "regularization_param = 3  # Helps to fight model overfitting.\n",
    "alpha = 0.1  # Gradient descent step size.\n",
    "\n",
    "# Init neural network.\n",
    "multilayer_perceptron = MultilayerPerceptron(x_train, y_train, layers, epsilon, normalize_data)\n",
    "\n",
    "# Train neural network.\n",
    "(thetas, costs) = multilayer_perceptron.train(regularization_param, max_iterations, alpha)\n",
    "\n",
    "plt.plot(range(len(costs)), costs)\n",
    "plt.xlabel('Gradient Steps')\n",
    "plt.ylabel('Cost')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Illustrate Hidden Layers Perceptrons\n",
    "\n",
    "Each perceptron in the hidden layer learned something from the training process. What it learned is represented by input theta parameters for it. Each perceptron in the hidden layer has 28x28 input thetas (one for each input image pizel). Each theta represents how valuable each pixel is for this particuar perceptron. So let's try to plot how valuable each pixel of input image is for each perceptron based on its theta values.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup the number of layer we want to display.\n",
    "# We want to display the first hidden layer.\n",
    "layer_number = 1 \n",
    "\n",
    "# How many perceptrons to display.\n",
    "num_perceptrons = len(thetas[layer_number - 1])\n",
    "\n",
    "# Calculate the number of cells that will hold all the images.\n",
    "num_cells = math.ceil(math.sqrt(num_perceptrons))\n",
    "\n",
    "# Make the plot a little bit bigger than default one.\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "# Go through the perceptrons plot what they've learnt.\n",
    "for perceptron_index in range(num_perceptrons):\n",
    "    # Extract perceptron data.\n",
    "    perceptron = thetas[layer_number - 1][perceptron_index][1:]\n",
    "\n",
    "    # Calculate image size (remember that each picture has square proportions).\n",
    "    image_size = int(math.sqrt(perceptron.shape[0]))\n",
    "    \n",
    "    # Convert image vector into the matrix of pixels.\n",
    "    frame = perceptron.reshape((image_size, image_size))\n",
    "        \n",
    "    # Plot the image matrix.\n",
    "    plt.subplot(num_cells, num_cells, perceptron_index + 1)\n",
    "    plt.imshow(frame, cmap='Greys', vmin=np.amin(frame), vmax=np.amax(frame))\n",
    "    plt.title('Percep. #%s' % perceptron_index)\n",
    "    plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
    "\n",
    "# Plot all subplots.\n",
    "plt.subplots_adjust(hspace=0.5, wspace=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Model Training Precision\n",
    "\n",
    "Calculate how many of training and test examples have been classified correctly. Normally we need test precission to be as high as possible. In case if training precision is high and test precission is low it may mean that our model is overfitted (it works really well with the training data set but it is not good at classifying new unknown data from the test dataset). In this case you may want to play with `regularization_param` parameter to fighth the overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Precision: 96.9412%\n",
      "Test Precision: 89.3000%\n"
     ]
    }
   ],
   "source": [
    "# Make training set predictions.\n",
    "y_train_predictions = multilayer_perceptron.predict(x_train)\n",
    "y_test_predictions = multilayer_perceptron.predict(x_test)\n",
    "\n",
    "# Check what percentage of them are actually correct.\n",
    "train_precision = np.sum(y_train_predictions == y_train) / y_train.shape[0] * 100\n",
    "test_precision = np.sum(y_test_predictions == y_test) / y_test.shape[0] * 100\n",
    "\n",
    "print('Training Precision: {:5.4f}%'.format(train_precision))\n",
    "print('Test Precision: {:5.4f}%'.format(test_precision))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Test Dataset Predictions\n",
    "\n",
    "In order to illustrate how our model classifies unknown examples let's plot first 64 predictions for testing dataset. All green digits on the plot below have been recognized correctly but all the red digits have not been recognized correctly by our classifier. On top of each digit image you may see the class (the number) that has been recognized on the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How many numbers to display.\n",
    "numbers_to_display = 64\n",
    "\n",
    "# Calculate the number of cells that will hold all the numbers.\n",
    "num_cells = math.ceil(math.sqrt(numbers_to_display))\n",
    "\n",
    "# Make the plot a little bit bigger than default one.\n",
    "plt.figure(figsize=(15, 15))\n",
    "\n",
    "# Go through the first numbers in a test set and plot them.\n",
    "for plot_index in range(numbers_to_display):\n",
    "    # Extract digit data.\n",
    "    digit_label = y_test[plot_index, 0]\n",
    "    digit_pixels = x_test[plot_index, :]\n",
    "    \n",
    "    # Predicted label.\n",
    "    predicted_label = y_test_predictions[plot_index][0]\n",
    "\n",
    "    # Calculate image size (remember that each picture has square proportions).\n",
    "    image_size = int(math.sqrt(digit_pixels.shape[0]))\n",
    "    \n",
    "    # Convert image vector into the matrix of pixels.\n",
    "    frame = digit_pixels.reshape((image_size, image_size))\n",
    "    \n",
    "    # Plot the number matrix.\n",
    "    color_map = 'Greens' if predicted_label == digit_label else 'Reds'\n",
    "    plt.subplot(num_cells, num_cells, plot_index + 1)\n",
    "    plt.imshow(frame, cmap=color_map)\n",
    "    plt.title(predicted_label)\n",
    "    plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
    "\n",
    "# Plot all subplots.\n",
    "plt.subplots_adjust(hspace=0.5, wspace=0.5)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
